Fine-Insured Bounties for Preventing Dangerous AI Development

post by hiAndrewQuinn (hiandrewquinn) · 2025-04-22T06:42:12.396Z · LW · GW · 0 comments

Contents

  Introduction to the Fine-Insured Bounty Concept
  Economic and Game-Theoretical Foundations
  Aligning Incentives for AI Safety
  Challenges and Limitations of the FIB Approach
  Application Scenarios in International Governance
  Comparison with Other Incentive-Based Safety Mechanisms
  Conclusion and Recommendations
    Recommendations for further research and pilot implementations include:
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Drafted with a Deep Research AI, then read through once for clarity and coherence.

This report aims to be much longer and comprehensive than my earlier 2022 essay sketching the idea. The most important new addition is discussing the international context explicitly, which I have previously been reticent to do.

Introduction to the Fine-Insured Bounty Concept

Fine-insured bounties (FIBs) are an incentive-based legal mechanism designed to deter wrongful or high-risk activities by aligning private incentives with public safety. In a FIB system, unlawful behavior is punished by a fine levied on the offender, and that fine is directly paid out as a bounty to whoever successfully reports or prosecutes the offense (Bounty Hunter Blackmail - by Robin Hanson - Overcoming Bias). If sufficiently large, each individual or organization is de facto required to carry insurance against such fines, ensuring that even if the perpetrator lacks funds, the fine (and thus the bounty) will be paid out by their insurer if the crime is committed. Thus creates a self-financing enforcement loop: wrongdoers fund the very rewards whistleblowers who turn them in get to pocket.

For example, suppose a state imposed such a $1,000 fine on anyone found to be littering, payable to whoever first reports the violation. Almost all would-be litterers would quickly realize that any passerby, security camera, etc. might be used to turn them in for the reward, making it personally too risky to continue that work. Any would-be bounty hunter would quickly realize that a $50 IP camera aimed at a well-trafficked area will likely earn its keep many times over. Entrepreneurs would quickly begin to offer litterer's insurance, covering 50% of the first littering accident each year at $5-10 per year for customers vetted to already be at low risk of littering. In a sense, the hunted becomes the hunter, and incentives are flipped so that ordinary people are empowered (and paid) to police activities deemed through the political process to be unacceptable.

In the context of artificial intelligence development, especially projects that could pose existential or catastrophic risks, fine-insured bounties offer a novel enforcement approach with some especially enticing properties. Traditional regulation of emerging technologies often struggles with information gaps and slow, centralized oversight. By contrast, FIBs decentralize the policing of banned activities – any informed party (employees, competitors, auditors, even private citizens) can profit from exposing a violation. This introduction outlines the FIB concept and sets the stage for a deeper exploration of its theoretical underpinnings and its applicability to preventing dangerous AI development. The sections that follow will review the economic and game-theoretic foundations of FIBs, explain how they align incentives for AI safety, discuss potential challenges and limitations, envision applications in international governance, compare FIBs with other incentive-based safety mechanisms, and finally offer conclusions and recommendations.

Economic and Game-Theoretical Foundations

At its core, the fine-insured bounty system is rooted in well-established principles of law & economics and game theory. Deterrence theory in economics (dating back to Gary Becker’s seminal work) holds that offenders can be dissuaded if the expected penalty for a wrongful act outweighs its potential benefit (Crime and Punishment: An Economic Approach). In formal terms, an optimal enforcement scheme sets the fine and the probability of detection such that “the evil of the punishment exceeds the advantage of the offense,” thereby nullifying the offender’s net expected gain. Fine-insured bounties (FIBs) put this into practice by both raising the probability of detection (via incentivized bounty hunters or whistleblowers) and by imposing fines commensurate with the harm of the prohibited act. In an ideal FIB regime, the government or society would set the fine for a given offense equal to the best estimate of the social harm caused by that offense (adjusted for the likelihood of it being caught) (Bounty Hunter Blackmail - by Robin Hanson - Overcoming Bias). This means a violator internalizes the full expected cost of the harm they might cause. A corresponding bounty is set to reward enforcers for catching the offense. The fine thus signals to the would-be offender (and their insurer) how hard they should work to prevent violations, and the bounty signals to others how hard to work to detect violations (Privately Enforced & Punished Crime - by Robin Hanson). Through this mechanism design, FIBs transform externalities (like the catastrophic risk of an unsafe AI) into direct expected costs for the actors involved in creating those risks.

Game theory helps illustrate how FIBs change the strategic calculus for groups. Consider a secretive project to develop a dangerous AI involving multiple researchers (a conspiracy of n collaborators). Each collaborator faces a choice: stay silent and continue the project, or defect and report the activity for a reward. Without special incentives, they might all remain silent if they believe the project’s success benefits them or if social pressures/NDAs hold them in check. However, introducing a substantial bounty for whistleblowing fundamentally alters this n-player prisoner’s dilemma. If each member has even a modest independent probability p of coming forward (due to fear or lure of reward), the probability that someone blows the whistle grows rapidly with n. In fact, the chance that at least one out of n conspirators reports the crime is 1 – p^n. For example, if each of 100 engineers has only a 1% chance of defecting, there is a 63% chance someone will come forward (since 1 – 0.99^100 ≈ 63%). If a bounty raises each individual’s inclination to speak up to, say, 3% per person (because the reward is very attractive), the overall chance of exposure jumps to ~95% (andrew-quinn.me). In other words, FIBs make large conspiracies inherently unstable: as groups grow, the likelihood of a leak approaches certainty unless loyalty is absolute. (A "mole" in this group would just be paying their own FIB back to themselves, which is a very nice property of the system against multi-agent conspiracies in particular.)

The insurance aspect of FIBs further reinforces game-theoretic stability. By requiring actors to buy insurance or post a bond against potential fines, the system creates a credible commitment device. Insurers will charge higher premiums to higher-risk clients and will monitor their behavior closely (to avoid paying out fines), adding another layer of oversight. Even suspicion short of full proof can carry economic consequences in this framework: for instance, an individual or lab with a history of ordering large numbers of GPUs (a telltale sign of heavy AI compute usage) might see their insurance premiums spike, reflecting the increased probability they are engaging in outlawed AI research (Fine-insured bounties as AI deterrent — LessWrong [LW · GW]). Those higher premiums, in turn, discourage risky activities and give bounty hunters leads to investigate (e.g. an insurer might quietly alert enforcers about a client’s unusual hardware purchases in exchange for partial consideration as a reporter in the potential FIB to follow). This dynamic leverages information economics: even asymmetric or probabilistic information (like a “low-fidelity suspicion”) is given a price and can be sold or used by bounty hunters (Fine-insured bounties as AI deterrent — LessWrong [LW · GW]). Through competitive forces, the FIB system aims to “break the blue wall of silence” that often protects wrongdoers, by pitting potential co-conspirators and even enforcement agents against each other in a race to collect bounties (Privately Enforced & Punished Crime - by Robin Hanson) (Privately Enforced & Punished Crime - by Robin Hanson). In equilibrium, very few perpetrators can feel confident of getting away with a crime when anyone (including one’s peers or subordinates) stands to gain from revealing it.

To summarize the theoretical foundations: fine-insured bounties integrate deterrence (Beckerian) economics with mechanism design. They ensure that the expected cost of a forbidden act (fine × probability of being caught) approximates or exceeds the act’s private benefit, thereby disincentivizing rational actors from attempting it (Crime and Punishment: An Economic Approach) (Bounty Hunter Blackmail - by Robin Hanson - Overcoming Bias). Simultaneously, they create a game-theoretic trap for conspirators – a classic collective action problem where defection is individually rewarded, undermining any collusive effort to develop a dangerous technology in secret (andrew-quinn.me) (andrew-quinn.me). By harnessing competition and self-interest, FIBs theoretically yield efficient, distributed enforcement in place of (or alongside) traditional government inspection and policing (Privately Enforced & Punished Crime - by Robin Hanson) (Expand Bounty Hunting - by Robin Hanson - Overcoming Bias).

Aligning Incentives for AI Safety

Applied to artificial intelligence development, fine-insured bounties could drastically realign the incentives of AI researchers, labs, and corporations toward safety and restraint. The current incentive landscape in AI is often described as an intense race: multiple companies and nations vie for breakthroughs, sometimes with inadequate regard for long-term safety, due to competitive pressure and profit motives. A FIB scheme introduces a powerful counter-pressure by attaching immediate personal and financial risk to unsafe AI development. In essence, it flips the script from “publish or perish” to “pause or perish.” Each AI engineer or executive must consider that pushing ahead recklessly could mean personal ruin: not only might their project be shut down, but they could be on the hook for multi-million-dollar fines, lose their required insurance coverage, or even face bankruptcy and career-ending legal penalties. Moreover, they could not fully trust their colleagues or employees to keep the secret—someone is always economically incentivized to be the canary in the coal mine. This fosters a culture of caution: when in doubt, do not build. As one advocate put it, it may be “easier to shift the Nash equilibrium of working on AI in the first place than it is to create safe AI” – meaning it could be more tractable to discourage people from entering a dangerous race at all, rather than expecting many actors to race but somehow do so safely (andrew-quinn.me). By making the act of developing an unaligned or unacceptably advanced AI personally unprofitable (or outright perilous) for researchers, FIBs shift the community’s behavior from open-risk-taking to prudence.

Under a FIB regime, AI developers and their organizations would effectively internalize the externality of AI risks. Normally, the harms from a rogue AI (e.g. a catastrophe caused by an uncontrollable superintelligence) would be borne by society at large, not the creators, especially if the creators acted in good faith. This moral hazard can lead to under-investment in safety. Fine-insured bounties change that: if creating a dangerous AI is illegal and subject to a fine equal to the anticipated social harm (potentially enormous, in the case of an existential threat), then any firm attempting such development would face an expected cost commensurate with risking humanity. In Robin Hanson’s formulation, if we set the fine for an AI-safety violation to our best estimate of the harm of one more such project (divided by the likelihood of being caught), the developer–insurer pair “would internalize that social harm”, giving them strong financial incentives to avoid that risky project altogether (Bounty Hunter Blackmail - by Robin Hanson - Overcoming Bias). In theory, this mirrors a Pigovian tax: just as a pollution tax makes a factory owner feel the cost of the pollution they emit, a colossal fine on unsafe AI makes an AI lab feel the expected cost of an AI disaster. The difference is that with FIBs the “tax” is not paid routinely to the government; it only materializes if someone violates the agreed-upon safety line and gets caught – at which point the payment goes to the enforcers. This conditional structure (no penalty if you stay safe, massive penalty if you don’t) creates a strong preventative incentive without continuously burdening those who comply.

The insurance mandate further aligns incentives by involving a third-party financial stakeholder – the insurer – whose profit motive is to prevent violations. An AI lab’s insurance provider would charge premiums based on the perceived risk of the lab’s activities (size of models being trained, compute resources, past safety track record, etc.) (Fine-insured bounties as AI deterrent — LessWrong [LW · GW]). If a lab wants to undertake a borderline project, the insurer might raise the premium sharply or even refuse coverage unless additional safety measures or oversight are put in place. Insurers would effectively become decentralized regulators, auditing their clients and advising them to minimize risky endeavors (since the insurer pays the fine if the client fails). Because insurers compete, they would innovate in monitoring methods: for instance, requiring automatic logging of compute usage, independent third-party safety audits for certain projects, or even installing “governors” on AI training runs that trigger alerts if defined limits are exceeded. The more a developer can prove their project is safe and within allowed bounds, the lower their premium – thus rewarding good behavior. In effect, the entire ecosystem of AI development would be suffused with watchdogs: co-workers eyeing large bounties, insurers surveilling compliance, and competitors ready to tip off authorities if someone cheats. All these elements align to tilt AI research toward safer, more transparent practices or outright delay of high-risk research. Indeed, financial incentives have driven much of the AI boom, and those same forces “can be used to stop them” when channeled via mechanisms like FIBs (andrew-quinn.me).

From a game-theoretic standpoint, fine-insured bounties change the payoff matrix for each individual involved in AI development. Suppose a talented AI researcher is weighing whether to join a well-funded but cutting-edge project that might produce an unaligned AGI. Without FIBs, the payoffs might be: a high salary, potential stock options, prestige if it succeeds – versus some abstract societal risk that they might rationalize away (and perhaps a low chance of personal liability if something goes wrong). With a FIB system in place, the calculus becomes much less favorable: the personal downside now includes being held legally responsible for an illicit project, enormous fines (far beyond any salary gains), and the likelihood that a colleague or even one’s future self could turn evidence over to claim the bounty (andrew-quinn.me) (andrew-quinn.me). (Notably, even turning oneself in is theoretically possible – one could report the conspiracy to collect the bounty, essentially paying the fine to oneself, leaving them no worse off financially but avoiding criminal liability and collecting the fines for their collaborators.) This means that even the members of a well-intentioned AI team are kept in check: if someone discovers their project has veered into illegal territory (say, the model being trained is more powerful than allowed), they have a strong incentive to blow the whistle early, both out of self-preservation and profit. And if they hesitate, they must worry that another team member will do so first. The result is a form of continuous internal accountability. Rather than relying solely on external audits or government inspections, the project is policed from within by the participants’ own incentives. Ideally, this steers everyone toward either staying below the danger thresholds (developing AI in safe regimes only) or investing heavily in alignment and safety to ensure their work remains legal and does not trigger catastrophic outcomes.

In summary, fine-insured bounties align individual and organizational incentives with AI safety by making unsafe development materially unattractive. They do so on multiple levels: deterring participation in risky projects, motivating internal whistleblowing and peer monitoring, and encouraging insurers and developers to proactively implement safety controls. The enthusiasm for this approach among some AI safety thinkers stems from its potential to solve the “race to the bottom” problem: it replaces the unstable equilibrium of mutual recklessness with an equilibrium of mutual restraint. When every actor knows that any attempt to leap ahead irresponsibly will likely be caught and punished (to the benefit of the whistleblower), the entire field can slow down and coordinate on safety, buying time to solve the harder problem of alignment. Financial carrots and sticks, properly structured, could thus complement moral appeals and formal regulations in keeping AI development on a safe track (andrew-quinn.me).

Challenges and Limitations of the FIB Approach

While the fine-insured bounty framework is theoretically compelling, it faces significant practical challenges and limitations when implemented in the real world, especially for something as complex as AI development. Key issues include enforcement feasibility, information problems, potential perverse incentives, and coordination hurdles:

• Enforcement and Legal Feasibility: For FIBs to work, there must be a reliable legal process to adjudicate violations and impose fines. This means defining at least somewhat clearly what constitutes the banned “dangerous technology” (e.g. training a model above a certain capability threshold, or developing AI systems without required safeguards) – a non-trivial task in itself, although some "I know it when I see it" level fuzziness may incur a useful chilling effect on borderline projects, see below. Once defined, authorities need the capacity to handle potentially numerous leads and evidence brought by bounty hunters. False or frivolous claims could burden the legal system, although bounty hunters are only paid upon successful conviction, which deters purely baseless accusations. Still, ensuring due process is crucial: accused parties must have fair trials so that bounties are not paid for wrongful convictions. Moreover, some fines may be enormous (if pegged to existential risks), raising questions of proportionality and enforceability. Courts might balk at enforcing a fine of, say, $100 million or more on an individual or small startup. To mitigate this, the system relies on insurance – but if an actor operates without insurance or exceeds their coverage, collection could be difficult. One proposal is to treat non-insured development or inability to pay the fine as a separate crime with strong penalties, and to allow work-program or payment plans for offenders to pay off fines over years or decades if needed (andrew-quinn.me). Even so, the willingness of real insurers to enter this market (and of jurisdictions to mandate such insurance) remains to be proven. The entire apparatus is legally unprecedented; smaller-scale pilot programs or simulations would likely be needed to work out kinks before applying it to AI globally.
• Information Asymmetries and Evasion: By design, FIBs leverage insider information and publicly observable signals to catch violations, but truly clandestine operations could still pose a problem. Highly motivated actors might work in extreme secrecy or solitude to evade detection. The historical argument in the AI case is that a single genius cannot build a world-ending AI alone in a basement – cutting-edge AI typically needs large teams and conspicuous resources (lots of computing power, funding, etc.) (andrew-quinn.me). To the extent this remains true, the FIB system’s net of informants and insurers has something to latch onto. However, if future AI progress makes it easier for small cells or even individuals to create dangerous systems (through more efficient algorithms or via stolen tools), the detection challenge grows. Information asymmetry means the developers know their own work far better than outsiders do; they may attempt to obfuscate their activities as legitimate research. The insurance premium mechanism (where even suspicions like unusual GPU purchases raise red flags) is meant to counter this by never letting smoke go unnoticed. Nonetheless, sophisticated actors might find ways to disguise their tracks or operate in jurisdictions without FIB laws (a loophole we discuss under coordination). There’s also the issue of technical proof: a bounty hunter might suspect a certain AI project is in violation, but obtaining hard evidence (e.g. the source code or weight files of a model) could be difficult if the perpetrators secure their data. This might necessitate novel investigative powers or digital surveillance authorized for bounty hunters, which in turn raises civil liberties concerns. On the other hand, the bounty hunter may choose to share a generous portion of the bounty with someone on the inside who does have access to such secure data. Striking the balance between empowering bounty hunters and protecting privacy is a delicate challenge, and Hanson notes this would require careful policy design for evidence handling and privacy rights (Privately Enforced & Punished Crime - by Robin Hanson)).
• Collusion, Blackmail, and Perverse Incentives: A known concern in any bounty-based enforcement system is the risk of collusion between enforcers and offenders. In the FIB scenario, the most worrying form is bounty hunter blackmail (Bounty Hunter Blackmail - by Robin Hanson - Overcoming Bias). If the fine significantly exceeds the bounty (as it generally would, since the fine covers total harm and enforcement cost, and the bounty is just the enforcer’s reward, then once a bounty hunter discovers a violation, both parties have an incentive to settle secretly. The bounty hunter could threaten to report the AI lab, but instead of going to court, agree to hush it up in exchange for a payment that they split with the offender’s insurer or the offender themselves. For example, if an offense carries a $50M fine nut only a $10M bounty, a whistleblower might approach the offending lab/insurer and negotiate a $30M payoff to keep things quiet – the insurer saves money (paying $30M instead of $50M), the whistleblower gets three times the official bounty (tax-free, under the table), and the public never learns of the violation (Bounty Hunter Blackmail - by Robin Hanson - Overcoming Bias). This kind of illegal side-deal undermines the whole premise of the FIB system, because the offense goes unpunished (at least officially) and undeterred. There are however strong inherent obstacles to such collusion – for instance, the conspirators would fear that another bounty hunter might independently catch the offense or that the blackmailer is bluffing or recording the interaction. Payments done in secret also carry the risk of continuous extortion, aka the blackmailer can keep coming back for more.
• Over-Deterrence and Innovation Chilling: While the goal is to chill truly dangerous tech development (e.g. a world-ending AI project), a blunt or overly broad FIB regime could also chill beneficial innovation. If fines are set too high or the definition of “dangerous AI” is vague, researchers might steer clear of any advanced AI work for fear of accidentally tripping a wire. This could slow progress in AI capabilities that are actually safe or could delay discoveries in AI alignment itself. Policymakers would need to draw the line carefully (for instance, perhaps only certain categories of AI research, like attempting to create self-improving AGI or training models above a compute threshold, are prohibited). Even then, a FIB system might produce a conservative bias in the R&D community – which is arguably desired in the short term for safety, but not if it stifles all AI research indefinitely. There is also a fairness concern: incumbents vs. startups. Large organizations might afford expensive insurance and thus signal credibility, whereas smaller players might be priced out of even harmless research due to insurers’ caution. Additionally, employees in the AI sector might feel distrusted or constrained, potentially driving talent away or underground. Proponents counter that if we truly believe unaligned AI is an existential threat, then a temporary innovation slowdown is acceptable (or even necessary) until robust safety measures are in place (Fine-insured bounties as AI deterrent — LessWrong [LW · GW]). Still, striking the right balance – deterring the bad without quashing the good – is a challenge requiring ongoing calibration of fines, bounties, and the legal definitions of violations.
• Global Coordination Problems: By far the largest challenge for using FIBs to prevent dangerous AI is that AI development is global. If only one country or a coalition of some countries adopt the fine-insured bounty approach while others do not, there is a risk that outlawed AI projects will simply migrate to jurisdictions where the rules are laxer. This is analogous to how some jurisdictions become havens for activities banned elsewhere. A robust solution to AI existential risk likely demands an international agreement or norm – essentially an AI non-proliferation treaty – wherein major tech powers jointly commit to restraining certain AI developments. Fine-insured bounties could be written into such a treaty as a keyhole enforcement mechanism. For instance, countries might all agree that training an AI model above X FLOPS or of certain dangerous capability is illegal, and each country enacts laws to fine offenders (developers and supporting personnel) an enormous sum, payable to informants. In effect, each signatory nation deputizes bounty hunters to uphold the treaty. There are precedents for multinational coordination of incentives: e.g., the international crime of piracy in past centuries was often countered by bounty-like rewards for privateers. However, achieving universal or near-universal adoption of FIB policies has steep political obstacles. Some governments might refuse to cooperate, either because they are racing for AI dominance or because they distrust a system that rewards foreign whistleblowers on their soil. If a major AI-capable nation stayed outside the treaty, it could undermine the whole effort. One optimistic angle is that economic self-interest could encourage broad participation: even nations eager for AI advancement might realize that an arms race with no safety could kill everyone, including them, so a mutually enforced pause is in their interest. The treaty could include provisions for extraterritorial enforcement to handle holdouts – for example, allowing bounty hunters to target nationals of non-signatory states if they travel or operate in signatory territory. (Given that many software-based businesses using or selling AI services operate multinational storefronts, this may be a more effective strategy at constraining bad actors than it first appears.) A violator from a non-compliant region could be apprehended in a compliant region that has FIB laws. In practice, extradition and legal jurisdiction issues abound here, and aggressive extraterritorial actions could cause geopolitical conflict. So, the preferred solution is to get as many key players on board as possible, possibly by building FIB-like enforcement into the fabric of international AI governance frameworks being negotiated now. Another coordination challenge is consistency: if different countries set wildly different fine levels or bounty rules, there could be confusion or loopholes (e.g., a bounty hunter might prefer to bring evidence to whichever country pays the most for a conviction). Lastly, there is the issue of trust: nations would need to trust that this bounty system will be used for genuine risk reduction, not as a pretext for industrial espionage or targeting rival labs unjustly. Transparency and sharing of convicting evidence among allies might alleviate suspicions. All told, implementing fine-insured bounties for AI safety on a global scale is difficult but by no means unimaginable – it would likely require the same level of international consensus and verification mechanisms that we see in nuclear arms control, adapted to the very different context of private-sector and academic AI research.

Pilot programs in less extreme domains (for example, a national law applying FIB enforcement to a smaller scale tech hazard, or whistleblower bounty enhancements for existing AI regulations) could provide empirical evidence on how people respond to such incentives, what the rates of false reporting or collusion are, and whether insurance companies can effectively monitor their clients. That data would be invaluable before scaling up to an existential risk context. Nonetheless, even with these hurdles, the FIB concept remains a provocative answer to the oft-asked question in AI governance: “Who will guard the guardians?” – with the answer being: self-interested guardians, armed with contracts and incentives, watching each other.

Application Scenarios in International Governance

Envisioning how fine-insured bounties could be applied in practice, we consider a few scenarios, particularly in an international or treaty-based context:

• Global AI Safety Treaty with FIB Enforcement: In this scenario, the world’s major AI-developing nations come together to negotiate a binding agreement that prohibits certain dangerous AI activities (for example, creating AI systems without proper safety verification, or pursuing “AGI” beyond a designated capability threshold until alignment is solved). To enforce this, the treaty incorporates a FIB system: each country enacts domestic legislation that mirrors the agreed prohibitions and sets very high fines for violations, with whistleblower/bounty payouts tied to those fines. An international coordination body could be established to share information among national authorities – for instance, if a tipster in Country A discovers evidence of a covert AI project in Country B, they could relay it to Country B’s bounty system (or perhaps claim the bounty in Country A which then liaises with B for enforcement). There might even be a global bounty pool funded by all parties for tips that are hard to localize (though funding via offender fines is the core idea, a supplementary pool could encourage whistleblowers to come forward even if they aren’t sure which jurisdiction to approach). The treaty could also facilitate standardized insurance requirements: AI labs worldwide might need to register and obtain liability insurance that complies with treaty guidelines, and insurers could share data on clients who attempt to dodge requirements.
• Unilateral or Plurilateral Implementation (Coalitions of the Willing): In absence of full global agreement, a subset of countries (or even a single leading country) might adopt the FIB approach and hope to set a de facto standard. For example, the United States and EU (plus allies) could implement joint legislation such that any entity within their jurisdictions caught building unacceptably dangerous AI faces severe fines and that whistleblowers will be richly rewarded. They could announce that any person in the world is eligible for the bounty if they provide actionable evidence, even if the evidence must be obtained through unconventional means. This might encourage cross-border whistleblowing: say an employee in a non-treaty country secretly leaks documents about their lab’s illegal AI project to a US authority to claim a reward. There are legal complexities (e.g., the US might indict foreign persons in absentia, or use sanctions to pressure foreign companies), but it’s not entirely without precedent – the U.S. Foreign Corrupt Practices Act, for instance, claims jurisdiction over bribery worldwide if it touches the US financial system, and has bounty provisions via the SEC whistleblower program that foreigners can and do use. In a similar way, a country could long-arm its AI safety law. While this is second-best to global consensus, it could at least create a shield around key regions: dangerous AI development would either have to occur in isolation from the West (limiting talent and resources available to it) or risk exposure and punishment by the bounty framework if any information crosses borders. The hope would be that this initial club of countries demonstrating FIB enforcement would eventually persuade or coerce others to join (through diplomatic pressure or showing that the system works without hampering innovation too much among members).
• Incorporating FIBs into Existing Frameworks: There are also softer ways FIBs could influence international governance. For example, discussions at the UN or the Global Partnership on AI could include recommendations for member states to strengthen whistleblower protections and rewards in AI oversight. Perhaps an international model law on AI safety is drafted, including an insurance-and-bounty enforcement mechanism, which interested countries can adopt domestically. Even without a formal treaty, if the leading AI hubs each independently put in place FIB laws (due to convergent understanding of the risks), that starts to resemble a coordinated regime. Additionally, insurance companies might form international consortia or reinsurance pools to handle the potentially huge liabilities involved. This private-sector cooperation would naturally spread norms across borders (for instance, if an insurer in one country refuses to cover a lab unless it abides by certain safety standards that are common internationally). In the realm of nuclear safety, insurers and global pools play a role (e.g., for nuclear reactors there are international insurance arrangements); something analogous could emerge for AI where insurers from multiple countries agree on what constitutes uninsurable, dangerous AI activity.
• Application to Other Dangerous Technologies: While our focus is AI, it’s worth noting that fine-insured bounties could be generalized to other domains of international concern, and doing so might even strengthen the concept via precedent. Areas like synthetic biology and pandemic pathogens come to mind. One could imagine a treaty where labs that perform certain gain-of-function experiments are subject to FIB enforcement, incentivizing scientists to report illicit bioresearch before it leaks a virus. If such a system proved workable for bio-risk (or even a smaller hazard like illegal dumping of toxic waste, etc.), it would build confidence that a similar approach could scale to AI. Conversely, lessons from any attempted implementation in one domain would inform the other. International governance is often about incremental trust-building – states might initially be wary of a radical enforcement scheme, but if a coalition demonstrates reduced risk in one area with FIBs, others may join in or allow it to be expanded to AI.

In all these scenarios, a common theme is that international cooperation is highly beneficial. The fine-insured bounty mechanism could be a powerful tool in a globally coordinated toolbox, but it is not a magic wand that bypasses geopolitics. If misapplied (e.g., used by one bloc to undermine the scientific progress of a rival bloc without genuine regard for safety), it could even heighten tensions. Therefore, any treaty or agreement embedding FIBs would need to come with mutual transparency and likely some escape valves – perhaps a clause that if AI capabilities dramatically change, the terms will be revisited (since what’s “dangerous” could shift). The scenarios above highlight that while the practical execution is complex, there are pathways to gradually introduce fine-insured bounties into the fabric of international AI governance, ideally moving from smaller coalitions or pilot implementations toward wider adoption as confidence grows.

Comparison with Other Incentive-Based Safety Mechanisms

Fine-insured bounties are one approach among several incentive-oriented strategies proposed to ensure safe development of powerful technologies. Here we provide a brief comparison with a few other mechanisms, highlighting how FIBs differ or could complement them:

• Traditional Regulation and Licensing: The conventional approach to restricting dangerous tech is through government licensing regimes, audits, and penalties imposed by regulators. For example, one might require labs to get a license to train AI models above a certain size, with violations punishable by fines or sanctions. The key difference is that traditional enforcement relies on state employees (inspectors, police, etc.) to catch violators, whereas FIBs rely on private enforcement via bounty hunters and whistleblowers. Traditional regulation can suffer from bureaucratic slowness, limited enforcement resources, or regulatory capture (companies lobbying to soften rules). Fine-insured bounties, by contrast, harness competitive incentives – anyone who is effective at uncovering violations gets paid, which can lead to more vigorous and ubiquitous enforcement (Privately Enforced & Punished Crime - by Robin Hanson) (Expand Bounty Hunting - by Robin Hanson - Overcoming Bias). A potential downside is that FIBs enforce all violations they can find, whereas regulators might exercise discretion, focusing on the most egregious cases and giving leeway for minor infractions; a pure bounty system might be too rigid in enforcing every letter of the law uniformly (Privately Enforced & Punished Crime - by Robin Hanson). In practice, a mix can be used: licensing to establish the legal boundaries, and FIBs to help enforce them, ensuring that the rules cannot be quietly flouted. Compared to simple fines set by regulators, FIBs formalize the idea of turning fines into rewards for informants – something already seen in part in agencies like the U.S. SEC’s whistleblower program, but FIBs would amplify that dramatically and tie it to a de facto insurance requirement.
• Whistleblower Reward Programs: Many jurisdictions have laws that pay whistleblowers a portion of fines or recovered damages when they report corporate misconduct (e.g., the False Claims Act in the US for fraud against the government, or SEC/CFTC programs for financial fraud). These share the same spirit as fine-insured bounties: using monetary rewards to encourage insiders to come forward. In fact, the success of such programs bolsters the case for FIBs. For instance, whistleblower cases have been responsible for the majority of fraud recoveries under the U.S. False Claims Act, bringing in tens of billions of dollars; by one account, 72% of the $46.5 billion recovered under the Act up to 2020 came from whistleblower-initiated actions (What is the False Claims Act? - National Whistleblower Center). This suggests people do respond to incentives and report malfeasance when the rewards are significant. The fine-insured bounty system essentially universalizes and heightens these incentives. A key difference is that typical whistleblower laws pay a fraction of the collected fine (e.g., 15-30% in the False Claims Act), whereas a pure FIB would pay close to 100% of the fine (minus any administrative fees) to the bounty claimant (Fine-insured bounties as AI deterrent — LessWrong [LW · GW]) (Bounty Hunter Blackmail - by Robin Hanson - Overcoming Bias). This is a much stronger incentive, but it also removes some flexibility (since in current programs the government can and does adjust bounty amounts or negotiate settlements without necessarily paying whistleblowers the maximum). Another difference: current programs rely on government prosecution of the case after the tip; a pure FIB system imagines private bounty hunters might themselves do the legwork to prove the offense in court. There is precedent for private prosecution in qui tam lawsuits, but extending that broadly is novel. In summary, whistleblower programs are a proven tool that FIBs build upon, making enforcement more automatic and market-driven. One could see FIBs as a generalization of whistleblower rewards combined with quasi-mandatory insurance to ensure whistleblowers always get paid with minimal enforcement overhead for the state in question.
• Liability Insurance and Bond Posting (without bounties): Another proposed mechanism for AI safety is to require developers to carry liability insurance or post a large bond that would pay out in the event their AI causes harm (similar to how nuclear power plants or vaccine manufacturers must have insurance). This is close to one half of the FIB idea – internalizing risk via insurance – but it doesn’t include the bounty hunter aspect. The insurance-only approach would mean if an AI accident happens, victims get paid, which is good for compensation but does not proactively prevent the accident if the actor believes the chance of an accident is low (or, crucial to this discussion here, if they believe Pascal's wager-style that an accident would mean no one would be around afterwards to chastise them anyway). What FIBs add is a way to penalize attempts at dangerous development well before an accident occurs, by defining the attempt as the trigger for fine payout to enforcers. Insurance alone might handle negligence post hoc, but FIBs create a preemptive enforcement incentive.
• “Windfall” Clauses or Taxes: One idea in the AI policy space has been the Windfall Clause, where AI developers voluntarily pledge to donate or redistribute a significant portion of any extremely large profits they earn in the future (the idea being to reduce the motive for racing unsafely by making the end prize less individually enriching). This is a sort of ex post incentive alignment (if AI yields enormous benefits, they’re shared) which might indirectly reduce willingness to take reckless risks. Compared to FIBs, windfall clauses are a carrot rather than a stick, and they rely on voluntary commitments rather than enforcement. They don’t directly help catch violations or accidents; they are more about ensuring fairness if AI succeeds. One could argue that windfall taxes or profit-sharing could complement FIBs: the latter deters catastrophic downsides, while the former manages the upside. Both are trying to address externalities (negative externalities of risk, positive externalities of public benefit) via financial mechanisms. However, windfall schemes don’t solve the core safety problem – a company might still race to AGI, just planning to pay the tax later. Only a direct deterrent (like FIB fines) stops the race in the first place.
• Prizes and Grants for Safe AI Development: Another incentive approach is offering prize competitions or grants for achieving specific safety milestones (for example, a huge reward for demonstrating a robust AI alignment technique, or for formally verifying an AI system’s safety properties). These encourage actors to pursue safety solutions and can channel talent into safety research. They don’t, however, discourage unsafe projects except by the opportunity cost of diverting people. Fine-insured bounties, in contrast, are about punishing and preventing harmful actions. In principle, a comprehensive policy might use both: discourage bad actions (via FIBs) and encourage good actions (via prizes, funding, recognition). The presence of a bounty system could even spur a market for safety solutions – e.g., companies might invest in tools that help them demonstrate compliance and avoid being flagged by bounty hunters. But fundamentally, prizes for safety are cooperative incentives, whereas FIBs are adversarial (pitting interests against each other for oversight). Both have roles, but they operate on different psychological and economic levers.
• Corporate Governance Measures and Self-Regulation: Some have suggested that tech companies could adopt internal policies like “red teams” to catch dangerous projects, internal bounties for employees who report policy violations, or commitments to pause if certain AI capability thresholds are reached. These are worthwhile, but they often lack teeth—an internal bounty might be tiny compared to what an external whistleblower law could provide, and self-policing only works if the organization’s leadership truly prioritizes safety over competitive advantage. Fine-insured bounties essentially externalize this function: even if a company’s culture fails to reward the employee who raises a concern, the legal system will reward them if they step outside and report it externally. This creates a backstop to corporate self-regulation. In effect, FIBs could enforce what some call a duty to the world that overrides any loyalty to one’s employer if the employer is endangering society.

In comparing these mechanisms, it’s clear that fine-insured bounties stand out for their enforcement strength and novelty. They aggressively leverage monetary incentives for deterrence through detection, arguably going farther than any existing system in making crime not pay. That very aggressiveness raises implementation concerns and requires robust legal infrastructure, as we discussed in prior sections. Other mechanisms like insurance or whistleblower laws are more tried-and-true but possibly insufficient for existential risks on their own. Therefore, rather than seeing these approaches in isolation, policy architects might consider integrating them. For example, a regime to prevent dangerous AI could include: licensing of advanced AI projects (traditional regulation), mandatory insurance for labs (internalizing risk), a fine-insured bounty system to empower whistleblowers (private enforcement), and government-funded bounties or prizes for those who contribute to AI safety research (encouraging solutions). Each piece addresses a different incentive problem: licensing sets the rules, insurance and FIBs enforce them via costs and vigilance, and prizes offer a positive goal to strive for (safe progress rather than any progress). The combination could be more robust than any single mechanism alone.

Conclusion and Recommendations

Fine-insured bounties represent a bold theoretical framework that reimagines how we might police the development of potentially catastrophic technologies like advanced AI. By tying together the threads of fines, insurance, and bounties, this mechanism seeks to align individual incentives with the global good, making it in each actor’s self-interest to refrain from dangerous development and to actively expose such behavior in others. The approach promises scalable and rigorous enforcement – “with sufficient competition and rewards, few could feel confident of getting away with [violations]” (Privately Enforced & Punished Crime - by Robin Hanson) – and it leverages existing economic principles of deterrence and market competition. If successful, it could fill a crucial gap in AI governance, namely the ability to credibly enforce safety norms in a decentralized and rapid manner across an entire industry and even internationally.

As this report has detailed, the FIB concept is not without significant challenges. Practical implementation would require careful legal design to avoid abuse (such as bounty hunters colluding with offenders) and to ensure fairness. It would also require international coordination at a level rarely seen outside of arms control treaties, given the global nature of AI development. These are non-trivial obstacles. The idea of essentially deputizing the world’s populace as AI watchdogs – and asking every AI developer to carry “misconduct insurance” or a very large stockpile of capital – will likely encounter political, cultural, and corporate resistance. Therefore, any path forward must include incremental experimentation and extensive dialogue among stakeholders.

Recommendations for further research and pilot implementations include:

• Theoretical Modeling and Simulation: Academia and think tanks should deepen the game-theoretic analysis of fine-insured bounties. More formal models could be developed to simulate how a coalition of AI firms might respond to various fine and bounty levels, or how collusion dynamics might play out in equilibrium. Economic modeling could also explore optimal calibration of fines vs. bounties to minimize perverse incentives (Privately Enforced & Punished Crime - by Robin Hanson). On the AI side, researchers could model how different “AI development race” scenarios (with multiple competing labs) change under the introduction of FIB enforcement. Such simulations can inform policymakers about the sensitivity of outcomes to key parameters (like detection probability, or fraction of actors that opt out of the system).
• Legal and Policy Design Work: Legal scholars should outline draft legislation or treaty language incorporating fine-insured bounties for AI. This would involve specifying the prohibited behaviors, the process for bounty claims, rights of the accused, and the insurance mandate. Edge cases need exploring: e.g., what if an AI research is conducted unwittingly (accidental violation), or how to handle an informant who was themselves deeply involved in the offense (as we suggested here, perhaps they merely pay the bounties back to themselves, making it much more profitable to be a mole). Clarifying these details in a hypothetical legal code would expose where complexities lie. Additionally, studying historical analogues – such as the use of privateers, or modern whistleblower statutes – can yield insights on best practices and pitfalls.
• Small-Scale Pilot Programs: Before trying this on AI, authorities could pilot a FIB-like system in a more contained domain. For instance, a government might institute a fine-insured bounty system to enforce environmental regulations (illegal dumping or emissions) or financial regulations (insider trading, but with bounties for co-conspirators who defect). Some areas of corporate law already head in this direction (e.g., the SEC whistleblower program pays 10-30% of fines for tips on securities violations), but a pilot could push it further: require firms in a high-risk sector to buy insurance that will pay bounties if the firm is caught violating certain rules. Observing how companies and insurers behave – Do they comply more? Do they attempt to bribe whistleblowers or retaliate? Do false reports clog the system? – would provide empirical evidence to either build confidence in, or caution about, applying the model to AI. If a pilot shows increase in detection of violations and manageable levels of noise, it strengthens the case for broader use.
• International Workshops and Coalition-Building: Policymakers from interested nations (perhaps those already concerned with AI risk) should hold joint discussions on enforcement mechanisms, including FIBs. Even if not everyone is ready to sign on, these conversations can surface concerns and allow the concept to be refined collaboratively. It may be wise to start with likeminded partners – for example, an “alliance” of democratic nations that share values on AI safety and the rule of law – to agree on mutual enforcement principles. This could later be expanded to a wider treaty. Engaging with industry will also be important: major AI labs and tech companies should be consulted. Some companies might resist, but others could see a FIB regime as preferable to more heavy-handed regulation or to an unbridled arms race that could destroy them all. In fact, forward-looking AI firms could proactively adopt internal policies that mirror FIB elements (e.g., setting aside a fund to reward employees for catching severe safety breaches) as a show of good faith and to experiment internally.
• Public Communication and Norm Setting: For something as unfamiliar as fine-insured bounties, public perception will matter. Framing it as a form of “AI whistleblower protection and reward act” might be more palatable than the somewhat esoteric term “fine-insured bounty.” Emphasizing the moral logic – that those who help prevent global catastrophes deserve to be rewarded, and those who recklessly court such catastrophes should pay – can rally public support. Over time, if small successes are achieved (say, a high-profile case where a dangerous effort was averted by a whistleblower who was rewarded handsomely), it could build a norm that this is the right approach for very high-stakes technology. This is similar to how public attitudes toward whistleblowers in finance or health care fraud became more positive as people saw them as serving the public interest (exposing fraud) rather than “snitching.” With AI, the stakes are even higher, and so the narrative of “incentivized guardians of humanity’s future” might resonate if communicated well.

In closing, fine-insured bounties offer a theoretically robust but untested tool in the quest to prevent dangerous AI developments. The approach aligns economic incentives with ethical outcomes, turning the surveillance and enforcement problem into a market opportunity for enterprising watchdogs. It could greatly augment our ability to enforce the hard limits that AI safety may require. As with any powerful tool, it must be handled with care: poorly implemented, it could create new problems or fail to achieve its purpose. The recommendation, therefore, is not to immediately impose FIBs worldwide, but to earnestly research, debate, and pilot this mechanism. As AI capability advances, the window for implementing effective governance may be narrow. Exploring fine-insured bounties now, in theory and practice, could pay off in the form of a ready-to-deploy system of incentives that keeps AI development safe and beneficial. In the best case, this framework – alongside other policy measures – would help ensure that humanity reaps the fruits of advanced AI without sowing the seeds of its destruction.

Sources:

1. Robin Hanson, “Privately Enforced & Punished Crime” – describing the fine-insured bounty (FIB) legal system (Bounty Hunter Blackmail - by Robin Hanson - Overcoming Bias) (Bounty Hunter Blackmail - by Robin Hanson - Overcoming Bias).
2. Robin Hanson, “Bounty Hunter Blackmail” – on preventing collusion between bounty hunters and offenders (Bounty Hunter Blackmail - by Robin Hanson - Overcoming Bias) (Bounty Hunter Blackmail - by Robin Hanson - Overcoming Bias).
3. “Fine-Insured Bounties as AI Deterrent” (LessWrong post by Virtual_Instinct) – applying FIBs to AI, with the Arkansas $1000 fine example and discussion of scaling effects (Fine-insured bounties as AI deterrent — LessWrong [LW · GW]) (Fine-insured bounties as AI deterrent — LessWrong [LW · GW]).
4. Andrew Quinn, “AI Bounties Revisited” – analysis of how bounties could discourage AI races, including conspiracy mathematics and shifting Nash equilibria (andrew-quinn.me) (andrew-quinn.me).
5. Gary S. Becker, Crime and Punishment: An Economic Approach – classic economics of crime, advocating setting expected punishment to outweigh gains (Crime and Punishment: An Economic Approach).
6. Empirical data on whistleblower programs, e.g. U.S. False Claims Act recoveries (What is the False Claims Act? - National Whistleblower Center), demonstrating the efficacy of financial incentives for exposing wrongdoing.
7. Discussion on implementation challenges and international coordination from various commentators (LessWrong, Hacker News) highlighting enforcement, information, and global issues (Privately Enforced & Punished Crime - by Robin Hanson) (Fine-insured bounties as AI deterrent — LessWrong [LW · GW]).

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